Integrating Biosystem Models Using Waveform Relaxation

Modelling in systems biology often involves the integration of component models into larger composite models. How to do this systematically and efficiently is a significant challenge: coupling of components can be unidirectional or bidirectional, and of variable strengths. We adapt the waveform relaxation (WR) method for parallel computation of ODEs as a general methodology for computing systems of linked submodels. Four test cases are presented: (i) a cascade of unidirectionally and bidirectionally coupled harmonic oscillators, (ii) deterministic and stochastic simulations of calcium oscillations, (iii) single cell calcium oscillations showing complex behaviour such as periodic and chaotic bursting, and (iv) a multicellular calcium model for a cell plate of hepatocytes. We conclude that WR provides a flexible means to deal with multitime-scale computation and model heterogeneity. Global solutions over time can be captured independently of the solution techniques for the individual components, which may be distributed in different computing environments.

[1]  Frank Uhlig,et al.  Numerical Algorithms with C , 1996 .

[2]  Leif H. Finkel,et al.  BIOENGINEERING MODELS OF CELL SIGNALING , 2007 .

[3]  A. Arkin,et al.  Stochastic mechanisms in gene expression. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[4]  A. Mogilner,et al.  Analysis of actin dynamics at the leading edge of crawling cells: implications for the shape of keratocyte lamellipodia , 2003, European Biophysics Journal.

[5]  David R. C. Hill,et al.  Theory of Modelling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems: Second Edition by B. P. Zeigler, H. Praehofer, T. G. Kim, Academic Press, San Diego, CA, 2000. , 2002 .

[6]  Kevin Burrage,et al.  Parallel and sequential methods for ordinary differential equations , 1995, Numerical analysis and scientific computation.

[7]  D. Lauffenburger,et al.  Receptors: Models for Binding, Trafficking, and Signaling , 1993 .

[8]  Bert Pohl On the convergence of the discretized multisplitting waveform relaxation algorithm , 1993 .

[9]  T. Tordjmann,et al.  Hormone receptor gradients supporting directional Ca2+ signals: direct evidence in rat hepatocytes. , 2003, Journal of hepatology.

[10]  Nicholas A. Peppas,et al.  Receptors: models for binding, trafficking, and signaling , 1996 .

[11]  J. M. Watt Numerical Initial Value Problems in Ordinary Differential Equations , 1972 .

[12]  T Höfer,et al.  Model of intercellular calcium oscillations in hepatocytes: synchronization of heterogeneous cells. , 1999, Biophysical journal.

[13]  Masaru Tomita,et al.  A multi-algorithm, multi-timescale method for cell simulation , 2004, Bioinform..

[14]  D. Gillespie A General Method for Numerically Simulating the Stochastic Time Evolution of Coupled Chemical Reactions , 1976 .

[15]  Bernard P. Zeigler,et al.  Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems , 2000 .

[16]  E Bornberg-Bauer,et al.  Switching from simple to complex oscillations in calcium signaling. , 2000, Biophysical journal.

[17]  Michael A. Gibson,et al.  Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels , 2000 .

[18]  Vipul Periwal,et al.  On Modules and Modularity , 2006 .

[19]  R Toral,et al.  Stochastic effects in intercellular calcium spiking in hepatocytes. , 2001, Journal of theoretical biology.

[20]  Adelinde M. Uhrmacher,et al.  Concepts of object- and agent-oriented simulation , 1997 .

[21]  Frank J. Bruggeman,et al.  Systems Biology: Philosophical Foundations , 2007 .

[22]  Christian Lubich,et al.  Multirate extrapolation methods for differential equations with different time scales , 1997, Computing.

[23]  J. Dormand,et al.  High order embedded Runge-Kutta formulae , 1981 .